metadata

base_model: google/gemma-2-9b-it
datasets:
  - nroggendorff/profession
language:
  - en
license: mit
tags:
  - trl
  - sft
  - art
  - code
  - adam
  - gemma
model-index:
  - name: pro
    results: []
pipeline_tag: text-generation

Profession LLM

Pro is a language model fine-tuned on the Profession dataset using Supervised Fine-Tuning (SFT) and Teacher Reinforced Learning (TRL) techniques.

Features

Utilizes SFT and TRL techniques for improved performance
Supports English language

Usage

To use the LLM, you can load the model using the Hugging Face Transformers library:

from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
import torch

bnb_config = BitsAndBytesConfig(
    load_in_4bit=True,
    bnb_4bit_use_double_quant=True,
    bnb_4bit_quant_type="nf4",
    bnb_4bit_compute_dtype=torch.bfloat16
)

model_id = "nroggendorff/gemma-pro"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, quantization_config=bnb_config)

prompt = "[INST] Write a poem about tomatoes in the style of Poe.[/INST]"
inputs = tokenizer(prompt, return_tensors="pt")

outputs = model.generate(**inputs)

generated_text = tokenizer.batch_decode(outputs)[0]
print(generated_text)

License

This project is licensed under the MIT License.